Co‐designing and building an expert‐elicited non‐parametric Bayesian network model: demonstrating a methodology using aBonamia Ostreaespread risk case study

Abstract

The development and use of probabilistic models, particularly Bayesian networks (BN), to support risk‐based decision making is well established. Striking an efficient balance between satisfying model complexity and ease of development requires continuous compromise. Codesign, wherein the structural content of the model is developed hand‐in‐hand with the experts who will be accountable for the parameter estimates, shows promise, as do so‐called nonparametric Bayesian networks (NPBNs), which provide a light‐touch approach to capturing complex relationships among nodes. We describe and demonstrate the process of codesigning, building, quantifying, and validating an NPBN model for emerging risks and the consequences of potential management decisions using structured expert judgment (SEJ). We develop a case study of the local spread of a marine pathogen, namely, Bonamia ostreae. The BN was developed through a series of semistructured workshops that incorporated extensive feedback from many experts. The model was then quantified with a combination of field and expert‐elicited data. The IDEA protocol for SEJ was used in its hybrid (remote and face‐to‐face) form to elicit information about more than 100 parameters. This article focuses on the modeling and quantification process, the methodological challenges, and the way these were addressed.

Bayesian networks for risk analysis and decision support

“…The article also provides practical guidance on and examples of considerations in balancing expert burden and elicitation accuracy. Hanea et al (2022) describe an iterative and collaborative approach that employs (semi) structured methods for eliciting, combining, and using expert judgments to build and parameterize BNs. They put a particular emphasis on codesigning the model structure with the same experts who quantify the parameters.…”

“…While formal protocols exist to quantify parameters of BNs after they are built, gaps remain even in well-defined procedures for model development. Two of the articles in this issue touch on this Nyberg et al (2021) and Hanea et al (2022) and propose semistructured approaches. There remains significant scope to further improve the scientific rigor in modeling approaches.…”

“…(2021) and Hanea et al. (2022) and propose semistructured approaches. There remains significant scope to further improve the scientific rigor in modeling approaches.…”